Hierarchical models of behavior and prefrontal function.

By Matthew Botvinick. 2008

470 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, methodology): “We use hierarchies to abstract away from ﬁne-grained diﬀerences, and capture more general commonalities between representations and behaviors (Botvinick, 2008; Tenenbaum et al., 2011), such as parts of an object, objects in a scene, neighborhoods in a town, and towns in a country.”
A Hierarchy of Time-Scales and the Brain (2008, unknown): “human behaviour, see [60] for a recent example and [61,62].”
Unity: A General Platform for Intelligent Agents (2018, background): “These complex tasks might display hierarchical structure, a hallmark of human intelligence (Botvinick, 2008), or vary from instance to instance, thus requiring meta-learning or generalization to solve (Wang et al., 2016).”
Cortical High-Density Counterstream Architectures (2013, background): “In this way, multiple distributed cortical hierarchies form a large-scale model of the cortex ( 17 ) that reflects the laminar integration of interareal connectivity into local circuits ( 18 ) and is relevant to sensory ( 17 , 19 , 20 ), motor ( 21 , 22 ), and cognitive ( 23 – 27 ) systems.”
Multi-Level Discovery of Deep Options (2017, background): “The ﬁeld of hierarchical reinforcement learning has a long history [9, 1, 10], and has also been applied in robotics [11, 12, 13] and in the analysis of biological systems [14, 15, 16, 17, 18].”
The Potential Benefits of Mindfulness Training in Early Childhood: A Developmental Social Cognitive Neuroscience Perspective (2012, background): “…of information via neural circuits that coordinate hierarchically arranged regions of PFC (Bunge & Zelazo, 2006; cf. Badre & D’Esposito, 2007; Botvinick, 2008; Christoff & Gabrieli, 2000; O’Reilly, 2010) is essential for the deliberate selection, activation (or deactivation), and…”
The Cognitive Neuroscience of Metacognition (2014, methodology): “[9] showed that déjà vu experiences arise when a familiarity experience occurs without corresponding retrieval of event details.”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “The functioning of this system is often described in terms of progressively more sophisticated mappings between stimuli (or stimuli plus task sets) and responses, possibly learned through reinforcement [9,21,40].”
Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex (2015, background): “In general, HRL models attempt to learn to accomplish tasks composed of abstract goals, rules, and concrete elementary behaviors organized into behavioral schemata (Barto & Mahadevan, 2003; Botvinick, 2008; Botvinick, Niv, & Barto, 2009; Jong & Stone, 2008).”
Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective (2008, background): “…Wood & Grafman, 2003; Zalla, Pradat-Diehl, & Sirigu, 2003), possibly in such a way that higher levels of structure are represented more anteriorly (Botvinick, 2008; Fuster, 2001, 2004; Haruno & Kawato, 2006; Koechlin et al., 2003) In addition to its default, top-level policy, the actor in HRL…”
Reinforcement learning in the brain (2009, background): “But, above and beyond the issue of learning of modules, the hierarchical RL framework raises many tantalizing questions regarding the neural implementation of hierarchical control (Botvinick, 2008; Botvinick, Niv, & Barto, 2008).”
Identifying the brain's most globally connected regions (2010, unknown): “Such highly convergent connectivity suggests RLPFCmay be important for integrating information from across the brain during complex cognitive tasks (Badre, 2008; Botvinick, 2008; Fuster, 2004; Wendelken et al., 2008).”
Optimal Behavioral Hierarchy (2014, background): “Simple actions fit together into coherent subtasks, which themselves combine to accomplish higher-level goals [5,6].”